Classes

Class	Line #	Total Statements	Complexity	TOTAL Coverage	Actions
Grouping	40	63	14	0.9204545692%	Show methods

Class Grouping

Class Grouping	Line # 40	Total Statements 63	Complexity 14	TOTAL Coverage 0.9204545692%
makeGroupsFrom(SequenceI[],String[],List<SequenceGroup>) : SequenceGroup[]   makeGroupsFrom(SequenceI[],String[],List<SequenceGroup>) : SequenceGroup[]	5252	27.027	6.06	0.9459459 0.945945994.6%
makeGroupsFromCols(SequenceI[],ColumnSelection,List<SequenceGroup>) : SequenceGroup[]   makeGroupsFromCols(SequenceI[],ColumnSelection,List<SequenceGroup>) : SequenceGroup[]	114114	36.036	7.07	0.9166667 0.916666791.7%
divideByFeature(String[],String[],int,int,SequenceI[],Vector,String) : void   divideByFeature(String[],String[],int,int,SequenceI[],Vector,String) : void	209209	0.00	1.01	-1.0 -1.0 -

 

Contributing tests

Contributing tests

Select all

	Test contribution	Test	Result	Actions
	0.92045456	jalview.analysis.GroupingTest.testMakeGroupsWithBothjalview.analysis.GroupingTest.testMakeGroupsWithBoth	1PASS

 

Source view

1		/*
2		* Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
3		* Copyright (C) $$Year-Rel$$ The Jalview Authors
4		*
5		* This file is part of Jalview.
6		*
7		* Jalview is free software: you can redistribute it and/or
8		* modify it under the terms of the GNU General Public License
9		* as published by the Free Software Foundation, either version 3
10		* of the License, or (at your option) any later version.
11		*
12		* Jalview is distributed in the hope that it will be useful, but
13		* WITHOUT ANY WARRANTY; without even the implied warranty
14		* of MERCHANTABILITY or FITNESS FOR A PARTICULAR
15		* PURPOSE. See the GNU General Public License for more details.
16		*
17		* You should have received a copy of the GNU General Public License
18		* along with Jalview. If not, see <http://www.gnu.org/licenses/>.
19		* The Jalview Authors are detailed in the 'AUTHORS' file.
20		*/
21		package jalview.analysis;
22
23		import jalview.datamodel.ColumnSelection;
24		import jalview.datamodel.SequenceGroup;
25		import jalview.datamodel.SequenceI;
26
27		import java.util.ArrayList;
28		import java.util.HashMap;
29		import java.util.List;
30		import java.util.Map;
31		import java.util.Vector;
32
33		/**
34		* various methods for defining groups on an alignment based on some other
35		* properties
36		*
37		* @author JimP
38		*
39		*/
		92% Uncovered Elements: 7 (88) Complexity: 14 Complexity Density: 0.22
40		public class Grouping
41		{
42		/**
43		* Divide the given sequences based on the equivalence of their corresponding
44		* selectedChars string. If exgroups is provided, existing groups will be
45		* subdivided.
46		*
47		* @param sequences
48		* @param selectedChars
49		* @param list
50		* @return
51		*/
		94.6% Uncovered Elements: 2 (37) Complexity: 6 Complexity Density: 0.22
52	1	public static SequenceGroup[] makeGroupsFrom(SequenceI[] sequences,...
53		String[] selectedChars, List<SequenceGroup> list)
54		{
55		// TODO: determine how to get/recover input data for group generation
56	1	Map<String, List<SequenceI>> gps = new HashMap<String, List<SequenceI>>();
57	1	int width = 0, i;
58	1	Map<String, SequenceGroup> pgroup = new HashMap<String, SequenceGroup>();
59	1	if (list != null)
60		{
61	1	for (SequenceGroup sg : list)
62		{
63	2	for (SequenceI sq : sg.getSequences(null))
64		{
65	5	pgroup.put(sq.toString(), sg);
66		}
67		}
68		}
69	6	for (i = 0; i < sequences.length; i++)
70		{
71	5	String schar = selectedChars[i];
72	5	SequenceGroup pgp = pgroup.get(((Object) sequences[i]).toString());
73	5	if (pgp != null)
74		{
75	5	schar = pgp.getName() + ":" + schar;
76		}
77	5	List<SequenceI> svec = gps.get(schar);
78	5	if (svec == null)
79		{
80	4	svec = new ArrayList<SequenceI>();
81	4	gps.put(schar, svec);
82		}
83	5	if (width < sequences[i].getLength())
84		{
85	1	width = sequences[i].getLength();
86		}
87	5	svec.add(sequences[i]);
88		}
89		// make some groups
90	1	SequenceGroup[] groups = new SequenceGroup[gps.size()];
91	1	i = 0;
92	1	for (String key : gps.keySet())
93		{
94	4	SequenceGroup group = new SequenceGroup(gps.get(key),
95		"Subseq: " + key, null, true, true, false, 0, width - 1);
96
97	4	groups[i++] = group;
98		}
99	1	gps.clear();
100	1	pgroup.clear();
101	1	return groups;
102		}
103
104		/**
105		* Divide the given sequences based on the equivalence of characters at
106		* selected columns If exgroups is provided, existing groups will be
107		* subdivided.
108		*
109		* @param sequences
110		* @param columnSelection
111		* @param list
112		* @return
113		*/
		91.7% Uncovered Elements: 4 (48) Complexity: 7 Complexity Density: 0.19
114	1	public static SequenceGroup[] makeGroupsFromCols(SequenceI[] sequences,...
115		ColumnSelection cs, List<SequenceGroup> list)
116		{
117		// TODO: determine how to get/recover input data for group generation
118	1	Map<String, List<SequenceI>> gps = new HashMap<String, List<SequenceI>>();
119	1	Map<String, SequenceGroup> pgroup = new HashMap<String, SequenceGroup>();
120	1	if (list != null)
121		{
122	1	for (SequenceGroup sg : list)
123		{
124	2	for (SequenceI sq : sg.getSequences(null))
125		{
126	5	pgroup.put(sq.toString(), sg);
127		}
128		}
129		}
130
131		/*
132		* get selected columns (in the order they were selected);
133		* note this could include right-to-left ranges
134		*/
135	1	int[] spos = new int[cs.getSelected().size()];
136	1	int width = -1;
137	1	int i = 0;
138	1	for (Integer pos : cs.getSelected())
139		{
140	3	spos[i++] = pos.intValue();
141		}
142
143	6	for (i = 0; i < sequences.length; i++)
144		{
145	5	int slen = sequences[i].getLength();
146	5	if (width < slen)
147		{
148	1	width = slen;
149		}
150
151	5	SequenceGroup pgp = pgroup.get(((Object) sequences[i]).toString());
152	5	StringBuilder schar = new StringBuilder();
153	5	if (pgp != null)
154		{
155	5	schar.append(pgp.getName() + ":");
156		}
157	5	for (int p : spos)
158		{
159	15	if (p >= slen)
160		{
161	0	schar.append("~");
162		}
163		else
164		{
165	15	schar.append(sequences[i].getCharAt(p));
166		}
167		}
168	5	List<SequenceI> svec = gps.get(schar.toString());
169	5	if (svec == null)
170		{
171	4	svec = new ArrayList<SequenceI>();
172	4	gps.put(schar.toString(), svec);
173		}
174	5	svec.add(sequences[i]);
175		}
176		// make some groups
177	1	SequenceGroup[] groups = new SequenceGroup[gps.size()];
178	1	i = 0;
179	1	for (String key : gps.keySet())
180		{
181	4	SequenceGroup group = new SequenceGroup(gps.get(key),
182		"Subseq: " + key, null, true, true, false, 0, width - 1);
183
184	4	groups[i++] = group;
185		}
186	1	gps.clear();
187	1	pgroup.clear();
188	1	return groups;
189		}
190
191		/**
192		* subdivide the given sequences based on the distribution of features
193		*
194		* @param featureLabels
195		* - null or one or more feature types to filter on.
196		* @param groupLabels
197		* - null or set of groups to filter features on
198		* @param start
199		* - range for feature filter
200		* @param stop
201		* - range for feature filter
202		* @param sequences
203		* - sequences to be divided
204		* @param exgroups
205		* - existing groups to be subdivided
206		* @param method
207		* - density, description, score
208		*/
		- Uncovered Elements: 0 (0) Complexity: 1 Complexity Density: -
209	0	public static void divideByFeature(String[] featureLabels,...
210		String[] groupLabels, int start, int stop, SequenceI[] sequences,
211		Vector exgroups, String method)
212		{
213		// TODO implement divideByFeature
214		/*
215		* if (method!=AlignmentSorter.FEATURE_SCORE &&
216		* method!=AlignmentSorter.FEATURE_LABEL &&
217		* method!=AlignmentSorter.FEATURE_DENSITY) { throw newError(
218		* "Implementation Error - sortByFeature method must be one of FEATURE_SCORE, FEATURE_LABEL or FEATURE_DENSITY."
219		* ); } boolean ignoreScore=method!=AlignmentSorter.FEATURE_SCORE;
220		* StringBuffer scoreLabel = new StringBuffer();
221		* scoreLabel.append(start+stop+method); // This doesn't work yet - we'd
222		* like to have a canonical ordering that can be preserved from call to call
223		* for (int i=0;featureLabels!=null && i<featureLabels.length; i++) {
224		* scoreLabel.append(featureLabels[i]==null ? "null" : featureLabels[i]); }
225		* for (int i=0;groupLabels!=null && i<groupLabels.length; i++) {
226		* scoreLabel.append(groupLabels[i]==null ? "null" : groupLabels[i]); }
227		* SequenceI[] seqs = alignment.getSequencesArray();
228		*
229		* boolean[] hasScore = new boolean[seqs.length]; // per sequence score //
230		* presence int hasScores = 0; // number of scores present on set double[]
231		* scores = new double[seqs.length]; int[] seqScores = new int[seqs.length];
232		* Object[] feats = new Object[seqs.length]; double min = 0, max = 0; for
233		* (int i = 0; i < seqs.length; i++) { SequenceFeature[] sf =
234		* seqs[i].getSequenceFeatures(); if (sf==null &&
235		* seqs[i].getDatasetSequence()!=null) { sf =
236		* seqs[i].getDatasetSequence().getSequenceFeatures(); } if (sf==null) { sf
237		* = new SequenceFeature[0]; } else { SequenceFeature[] tmp = new
238		* SequenceFeature[sf.length]; for (int s=0; s<tmp.length;s++) { tmp[s] =
239		* sf[s]; } sf = tmp; } int sstart = (start==-1) ? start :
240		* seqs[i].findPosition(start); int sstop = (stop==-1) ? stop :
241		* seqs[i].findPosition(stop); seqScores[i]=0; scores[i]=0.0; int
242		* n=sf.length; for (int f=0;f<sf.length;f++) { // filter for selection
243		* criteria if ( // ignore features outwith alignment start-stop positions.
244		* (sf[f].end < sstart || sf[f].begin > sstop) || // or ignore based on
245		* selection criteria (featureLabels != null &&
246		* !AlignmentSorter.containsIgnoreCase(sf[f].type, featureLabels)) ||
247		* (groupLabels != null // problem here: we cannot eliminate null feature
248		* group features && (sf[f].getFeatureGroup() != null &&
249		* !AlignmentSorter.containsIgnoreCase(sf[f].getFeatureGroup(),
250		* groupLabels)))) { // forget about this feature sf[f] = null; n--; } else
251		* { // or, also take a look at the scores if necessary. if (!ignoreScore &&
252		* sf[f].getScore()!=Float.NaN) { if (seqScores[i]==0) { hasScores++; }
253		* seqScores[i]++; hasScore[i] = true; scores[i] += sf[f].getScore(); //
254		* take the first instance of this // score. } } } SequenceFeature[] fs;
255		* feats[i] = fs = new SequenceFeature[n]; if (n>0) { n=0; for (int
256		* f=0;f<sf.length;f++) { if (sf[f]!=null) { ((SequenceFeature[])
257		* feats[i])[n++] = sf[f]; } } if (method==FEATURE_LABEL) { // order the
258		* labels by alphabet String[] labs = new String[fs.length]; for (int
259		* l=0;l<labs.length; l++) { labs[l] = (fs[l].getDescription()!=null ?
260		* fs[l].getDescription() : fs[l].getType()); }
261		* jalview.util.QuickSort.sort(labs, ((Object[]) feats[i])); } } if
262		* (hasScore[i]) { // compute average score scores[i]/=seqScores[i]; //
263		* update the score bounds. if (hasScores == 1) { max = min = scores[i]; }
264		* else { if (max < scores[i]) { max = scores[i]; } if (min > scores[i]) {
265		* min = scores[i]; } } } }
266		*
267		* if (method==FEATURE_SCORE) { if (hasScores == 0) { return; // do nothing
268		* - no scores present to sort by. } // pad score matrix if (hasScores <
269		* seqs.length) { for (int i = 0; i < seqs.length; i++) { if (!hasScore[i])
270		* { scores[i] = (max + i); } else { int nf=(feats[i]==null) ? 0
271		* :((SequenceFeature[]) feats[i]).length;
272		* jalview.bin.Console.errPrintln("Sorting on Score: seq "+seqs[i].getName()+
273		* " Feats: "+nf+" Score : "+scores[i]); } } }
274		*
275		* jalview.util.QuickSort.sort(scores, seqs); } else if
276		* (method==FEATURE_DENSITY) {
277		*
278		* // break ties between equivalent numbers for adjacent sequences by adding
279		* 1/Nseq*i on the original order double fr = 0.9/(1.0*seqs.length); for
280		* (int i=0;i<seqs.length; i++) { double nf; scores[i] =
281		* (0.05+fr*i)+(nf=((feats[i]==null) ? 0.0 :1.0*((SequenceFeature[])
282		* feats[i]).length));
283		* jalview.bin.Console.errPrintln("Sorting on Density: seq "+seqs[i].getName()+
284		* " Feats: "+nf+" Score : "+scores[i]); }
285		* jalview.util.QuickSort.sort(scores, seqs); } else { if
286		* (method==FEATURE_LABEL) { throw new Error("Not yet implemented."); } } if
287		* (lastSortByFeatureScore ==null ||
288		* scoreLabel.equals(lastSortByFeatureScore)) { setOrder(alignment, seqs); }
289		* else { setReverseOrder(alignment, seqs); } lastSortByFeatureScore =
290		* scoreLabel.toString();
291		*/
292		}
293
294		}